Study On Theory And Method Of Manufacturing Procurement DSS Based On Data Mining

With wildly use of ERP, CRM and MRP-II systems in manufacturing enterprises, a large amount of historical transactions records have been accumulated in databases without analysis or integration. It is useless to the managers, not mentioned helping decision making. But the procurement department has to make quick response to the challenge market everyday. So it is necessary to build decision support system in order to help them making excellent choice with information extracted from historical transactions records.A new decision support system is introduced with integration of data warehouse, OLAP and data mining module which was developed by requirements analysis of manufacturing enterprises procurement. It can provide managers with traditional decision making functions, multi-dimensional data analysis and association rules mining from transaction records.A procurement data warehouse is built through conceptual model design, logical model design and physical model design. An OLAP analysis cube of sales, purchase and supply is realized following DTS and ETL by using Analysis services tools provided by MS SQL server which enable visual analysis for enterprises.For transaction databases usually grow over time and the association rules mined from them must be re-evaluated and some new association rules may be generated and some old ones may become invalid. An incremental IFUFP-tree maintenance algorithm is presented with modification of the FP-tree construction algorithm for efficiently handling new transactions. The original database and new inserted transactions are considered in four cases and the results are then put into the IFUFP thus efficient maintenance association rules can be achieved. Besides, the counts of the sorted frequent items are also kept in the header table which is the same as FP-tree algorithm. Bi-directional links will fasten the maintenance processes of association rules through which high proficiency DSS system can be reached.A novel tree structure, called IFP-tree (improved pattern tree), which captures database information with one database scan and provides the same mining performance as the FP-growth method. The IFP-tree introduces the concept of dynamic tree restructuring to generate a highly compact frequency-descending tree structure at runtime following insertion phase and restructuring phase. An efficient tree restructuring method that restructures a prefix-tree branch-by-branch is also proposed. Extensive experimental results show that the IFP-tree is efficient for incremental mining with a single database scan that improves the DSS function.